We want to build an internal AI for our company's documentation. Should we Fine-Tune an open-source model like Llama 3 on our PDFs, or should we use Retrieval-Augmented Generation (RAG)? I’m worried about Hallucinations and the cost of retraining the model every time we update a document. Which approach provides higher accuracy for fact-based queries?
3 answers
For fact-based queries on dynamic data, RAG is almost always superior. In a RAG workflow, the model "looks up" the relevant parts of your PDFs in a Vector Database and uses that information as context for its answer. This drastically reduces Hallucinations because the model is forced to cite its sources. Fine-Tuning is better for changing the style or behavior of a model, but it’s terrible at "remembering" specific new facts.
RAG also provides an "Audit Trail." Because the model tells you which document it used to generate the answer, you can verify the output. In a fine-tuned model, the knowledge is baked into the weights, making it a "Black Box" where you can't tell where a fact came from.
Another benefit of RAG is that you don't have to retrain the model. If a document changes, you just update the index in your Vector Database. With Fine-Tuning, your model starts becoming "stale" the moment your data changes, and the compute costs for retraining can be prohibitive for small teams.
We use a hybrid approach. We Fine-Tune for the specific medical terminology of our field so the model understands the jargon, but we use RAG to pull the actual patient data for summaries.
This is critical for legal or medical compliance. "Show your work" is the best defense against AI errors.